As expected, the naive data loader (num_workers = 0) performs far worse, as loading the full batch syncronously blocks the training step. As we increase the number of workers, we notice a steady improvement until 3-4 workers, where the data loading time starts to increase.
Jan 02, 2019 · When num_workers>0, only these workers will retrieve data, main process won't. So when num_workers=2 you have at most 2 workers simultaneously putting data into RAM, not 3. Well our CPU can usually run like 100 processes without trouble and these worker processes aren't special in anyway, so having more workers than cpu cores is ok.
Mar 01, 2017 · It depends on the batch size, but I wouldn’t set it to the same number - each worker loads a single batch and returns it only once it’s ready. num_workers equal 0 means that it’s the main process that will do the data loading when needed, num_workers equal 1 is the same as any n, but you’ll only have a single worker, so it might be slow
Disable UserWarning for DataLoaders with num_workers=0 - Python pytorch-lightning Proposed refactoring or deprecation Disable dataloader UserWarning for num_workers=0 since 0 disables multi-process loading (according to pytorch docs it loads in the main process whereas 1 would create a separate process) Motivation
num_workers=0 means ONLY the main process will load batches (that can be a bottleneck). num_workers=1 means ONLY one worker (just not the main process) will load data but it will still be slow. The num_workers depends on the batch size and your machine. A general place to start is to set num_workers equal to the number of CPU cores on that machine.
24.06.2020 · When I use num_workers > 0 in DataLoader I obviosly use shared memory through Pytorch multiprocessing. It's roughly 0.5gb * 12 workers = 6gb of shared memory (/dev/shm in df -h). However, after every epoch this number grows bigger and bigger. On epoch 1 I consume 6gb of shared memory. After 2nd epoch I consume 12gb, after 3rd 18gb and so on.
The num_workers depends on the batch size and your machine. A general place to start is to set num_workers equal to the number of CPU cores on that machine. You can get the number of CPU cores in python using os.cpu_count() , but note that depending on your batch size, you may overflow RAM memory.
Sep 22, 2021 · Num_workers tells the data loader instance how many sub-processes to use for data loading. If the num_worker is zero (default) the GPU has to weight for CPU to load data. Theoretically, greater the...
Dataloader(num_workers=N), where N is large, bottlenecks training with DDP… ie: it will be VERY slow or won’t work at all. This is a PyTorch limitation. Forces everything to be picklable. There are cases in which it is NOT possible to use DDP. Examples are: Jupyter Notebook, Google COLAB, Kaggle, etc. You have a nested script without a root package
22.09.2021 · PyTorch num_workers, a tip for speedy training Talha Anwar Sep 22 · 2 min read There is a huge debate what should be the optimal num_workers for your dataloader. Num_workers tells the data loader...
01.01.2019 · When num_workers>0, only these workers will retrieve data, main process won't.So when num_workers=2 you have at most 2 workers simultaneously putting data into RAM, not 3.; Well our CPU can usually run like 100 processes without trouble and these worker processes aren't special in anyway, so having more workers than cpu cores is ok.
01.03.2017 · It depends on the batch size, but I wouldn’t set it to the same number - each worker loads a single batch and returns it only once it’s ready. num_workers equal 0 means that it’s the main process that will do the data loading when needed, num_workers equal 1 is the same as any n, but you’ll only have a single worker, so it might be slow 36 Likes